Multiscale Functional Imaging of Interfaces through Atomic Force Microscopy Using Harmonic Mixing

Abstract

The spatial resolution of atomic force microscopy (AFM) needed to resolve material interfaces is limited by the tip-sample separation ( d) dependence of the force used to record an image. Here, we present a new multiscale functional imaging technique that allows for in situ tunable spatial resolution, which can be applied to a wide range of inhomogeneous materials, devices, and interfaces. Our approach uses a multifrequency method to generate a signal whose d-dependence is controlled by mixing harmonics of the cantilever's oscillation with a modulated force. The spatial resolution of the resulting image is determined by the signal's d-dependence. Our measurements using harmonic mixing (HM) show that we can change the d-dependence of a force signal to improve spatial resolution by up to a factor of two compared to conventional methods. We demonstrate the technique with both Kelvin probe force microscopy (KPFM) and bimodal AFM to show its generality. Bimodal AFM with harmonic mixing actuation separates conservative from dissipative forces and is used to identify the regions of adhesive residue on exfoliated graphene. Our electrostatic measurements with open-loop KPFM demonstrate that multiple force modulations may be applied at once. Further, this method can be applied to any tip-sample force that can be modulated, for example, electrostatic, magnetic, and photoinduced forces, showing its universality. Because HM enables in situ switching between high sensitivity and high spatial resolution with any periodic driving force, we foresee this technique as a critical advancement for multiscale functional imaging.

Quantitative phase‐mode electrostatic force microscopy on silicon oxide nanostructures

“…These techniques were proven useful in studying the localization of trapped charges in thin films (Silveira & Marohn, 2004; Chen et al ., 2005a; Chen et al ., 2005b; Muller & Marohn, 2005), quantum dots (Tevaarwerk et al ., 2005) and nanotubes (Chin et al ., 2008); to measure the resistance at metal–semiconductor interfaces and grain boundaries in operating devices (Annibale et al ., 2007); to relate electrical properties, such as dielectric permittivity (Gramse et al ., 2009; El Khoury et al ., 2016; Fumagalli et al ., 2018), conductivity (Castellano‐Hernández & Sacha, 2015; Aurino et al ., 2016), piezoelectricity (Moon et al ., 2017) and percolation pathways (Barnes & Buratto, 2018), directly to the organization of the material at the mesoscopic length scales. Charge distribution in supramolecular architectures (Dabirian et al ., 2009; Borgani et al ., 2014; Garrett et al ., 2018), biomolecules (Gil et al ., 2002; Cuervo et al ., 2014; Dols‐Perez et al ., 2015; Lozano et al ., 2018; Lozano et al ., 2019), living organism (Esteban‐Ferrer et al ., 2014; Van Der Hofstadt et al ., 2016a; Van Der Hofstadt et al ., 2016b) and 2D materials (Collins et al ., 2013; Miyahara et al ., 2015; Shen et al ., 2018; Altvater et al ., 2019) was recently addressed with these techniques. The information obtained can be used as input for the design and optimization of device layouts, and ultimately for the simulation of functional devices and circuits.…”

Machine Learning for Perovskites' Reap-Rest-Recovery Cycle

“…11 Because most perovskites present inhomogeneities at the nano-and microscale, microscopic techniques must be further developed to resolve the relationship between composition, morphology, optical response, and electrical behavior at the intragrain and intergrain length scales. [34][35][36][37][38] Figure 3 displays examples of how microscopic methods have been implemented to help elucidate the dynamic response of this emerging material. Through environmentally controlled micro-PL, the effect of ambient gas and vacuum was identified, showing that the presence of O 2 can lead to an order of magnitude increase in radiative recombination; however, as shown in Figure 3A, not all grains behave identically and the phenomenon is facet dependent.…”

Quantitative mapping of magnetic properties at the nanoscale with bimodal AFM

“…bimodal AFM provided high-spatial resolution and accurate nanomechanical property maps of a large variety of materials and interfaces such as proteins, 11,21,24,25 DNA, [26][27][28] cells, 29,30 bone microconstituents, 31 lipid bilayers, 32,33 liposomes loaded with nanoparticles, 34 self-assembled monolayers, 35 virus, 36 2D materials, 37 organic crystals, 38,39 solid-liquid interfaces 40 and a variety of polymer surfaces. 22,23,41,42 Bimodal AFM was also applied to generate material contrast images on heterogeneous samples made of regions with different electrical [43][44][45][46][47][48][49] or magnetic properties. [50][51][52][53][54] However, in those experiments and in particular in magnetic samples, it was not possible to determine the magnetic moment or the magnetic field of the material.…”